Localization and tracking of medical instruments is an integral part of computer-assisted interventions and several technologies have been introduced for this purpose. Depending on the underlying working principles, each technology imposes some limitation on the usability of the tracking system. For example, electromagnetic (EM) tracking technology, in which known local EM fields are generated by transmitters in order to localize EM sensors (trackers) placed within the field, using the principle of mutual induction, has been integrated into various commercial products, and applied in several interventions. This popularity is primarily due to freedom from line-of-sight restrictions, small sensor size, and the ability to track instruments inside the patient's body, which is particularly helpful for guiding needles, catheters, and guide-wires, during insertions or placements.
However, EM trackers are highly susceptible to distortions caused by magnetic or electrically-conductive objects located in the close proximity of the tracking volume. In a clinical environment, such sources of field distortion include medical imaging devices (C-arm, CT gantry, etc.), equipment (tables, monitors, etc.), and instruments. The interference causes tracking errors ranging from a few millimeters in research environments to a few centimeters in clinical environments, especially in the presence of certain equipment, such as a C-arm fluoroscopy, known to significantly distort the EM field while unavoidable in many procedures.
Unless compensated for, the tracking error due to field distortion compromises the outcome of medical procedures and limits the reliability and utility of EM tracking in clinical settings. If this limitation is alleviated, EM tracking technology can open its way into new potential clinical procedures currently not possible. Current approaches used to reduce the impact of field distortion can be classified into three categories: reduction, detection, and compensation.    1) Reduction: These methods can help reduce the amount of field distortion caused by field distorting objects. For existing EM tracking systems, these methods may include object shielding, EM field type modification (e.g., from AC to pulsed-DC), or optimum positioning (patient, devices, and EM transmitter).    2) Detection: These techniques can help detect or monitor the amount of field distortion, or confirm if a tool can be tracked using EM trackers. They mostly rely on measurements of the induced currents in various (and normally redundant) coils or sensors. For example, a redundant EM sensor is placed at a known fixed location with respect to another sensor or the transmitter, and the variations of the measured location of the redundant sensor compared to its known fixed location are used to detect or monitor the amount of field distortion.    3) Compensation: These methods can help compensate for the field distortion and can further be classified into two sub-categories: static pre-calibration, and fusion.            i. Static pre-calibration is useful in environments where field distorting objects are stationary and the amount of distortion at each point in the tracking volume is invariable. In this case, a distortion map can be generally created pre-operatively in order to establish a relationship between each point in the workspace and the corresponding magnitude of distortion at that point. The created distortion map can be stored in various forms such as look-up tables, polynomial fitting, interpolation, or neural networks, or other methods. This map can subsequently be used intra-operatively to compensate for the undesired static distortion.        ii. Fusion approaches rely on redundant and independent (undistorted) measurements, such as alternative types of tracking systems (optical, imaging, inertial, etc.) or an array of EM transmitters and/or sensors fixed at known spatial coordinates. The distortion map is then created based on fusion of the information provided by the alternative tracker, or the assumption that the sensor array remains partially undistorted. This map can subsequently be used to compensate for field distortion.        
While the currently available methods for field distortion compensation may offer improved tracking performance, they as well impose severe constraints and incorporate substantial underlying limitations. Reduction and detection methods may be able to minimize the error due to a specific field distorting tool. However, these techniques generally lack the ability to compensate for the errors in the entire workspace or due to multiple field distorting objects. Static pre-calibration is a time consuming procedure as the sensor must be positioned at numerous points in the workspace. It also requires ground truth measurements by means of manual positioning, calibration phantom, robotic arm, or other tracking devices. Even after such a lengthy and tedious calibration procedure, the map is valid only for a static environment. Therefore, this method is unable to cope with the dynamic nature of most clinical settings. As a result, during the clinical procedure, this resource intensive calibration process should be repeated for every field distortion change, and a critical portion of the workspace where the patient is positioned may be inaccessible during such consequent recalibrations. Overall, in true clinical settings with dynamic changes due to movement or operation of imaging devices, surgical tools, tables, monitors, etc., this approach is ineffective and may in fact exacerbate the tracking error if the distortion map is outdated. Fusion-based methods are inefficient in nature as they either demand external tracking technologies, or an unnecessarily large number of redundant elements, in which case the field distortion is also determined accurately only in the vicinity of the redundant elements mounted typically on the exterior surface of the workspace and away from the region of interest. Furthermore, these methods are costly, may introduce additional errors due to spatial/temporal calibration between the tracking systems or sensors, and overcrowd the surgical site with excessive devices.
Accordingly, there is a need for effective detection and compensation for field distortion error.